@TechReport{Derner-TR-2014-25,
IS = { zkontrolovano 20 Jan 2015 },
UPDATE = { 2015-01-20 },
author = {Derner, Erik and Svoboda, Tom{\'a}{\v s}},
title = {Indexing Images for Visual Memory by Using {DNN} Descriptors
-- Preliminary Experiments},
institution = {Center for Machine Perception, K13133 FEE Czech Technical
University},
address = {Prague, Czech Republic},
year = {2014},
month = {December},
type = {Research Report},
number = {CTU--CMP--2014--25},
issn = {1213-2365},
pages = {16},
figures = {11},
authorship = {50-50},
psurl = {[Derner-TR-2014-25.pdf]},
project = { FP7-ICT-609763, SGS13/142/OHK3/2T/13},
annote = {Visual memory in mobile robotics is important to make the
local- ization of a robot robust to situations, when GPS or
similar localization methods are not available. Unlike many
conventional approaches us- ing local features, we use a
holistic method that employs deep neural networks (DNNs) to
calculate a global descriptor of the whole image. We
consider a scenario in which a robot equipped with an omni-
directional camera calculates and stores DNN descriptors of
images together with the positions as it moves in the
environment. When the position is unknown to the robot, the
algorithm estimates it for a given omnidirectional image by
matching it with the most similar database image. We
compared our approach with a recently tested GIST-based ap-
proach on the same dataset and we found out that the
DNN-based approach yields better results. The experiments
also show that the DNN-based algorithm is quite robust to
partial occlusion, rotation and changes in lighting
conditions.},
keywords = {Image indexing, visual localization, deep neural networks},
comment = { },
}